Difference between revisions of "RTAB-Map"
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== Source code == | == Source code == | ||
The code was tested on Windows (Xp, 7), Mac OS X 10.6 and Ubuntu 10.4LTS. | The code was tested on Windows (Xp, 7), Mac OS X 10.6 and Ubuntu 10.4LTS. | ||
| − | * Standalone application and | + | * Standalone application, libraries and ROS packages : [http://rtabmap.googlecode.com rtabmap] |
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<div style="text-align: center;"> | <div style="text-align: center;"> | ||
[[File:RTAB-Map_Interface.png|800px|Images acquired in Need For Speed Most Wanted]] | [[File:RTAB-Map_Interface.png|800px|Images acquired in Need For Speed Most Wanted]] | ||
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== Code source == | == Code source == | ||
Le code a été testé sur Windows (Xp, 7), Mac OS X 10.6 et Ubuntu 10.4LTS. | Le code a été testé sur Windows (Xp, 7), Mac OS X 10.6 et Ubuntu 10.4LTS. | ||
| − | * Logiciel "stand-alone" | + | * Logiciel "stand-alone", bibliothèques logicielles et noeuds ROS : [http://rtabmap.googlecode.com rtabmap] |
| − | |||
<div style="text-align: center;"> | <div style="text-align: center;"> | ||
[[File:RTAB-Map_Interface.png|800px|Images provenant de Need For Speed Most Wanted]] | [[File:RTAB-Map_Interface.png|800px|Images provenant de Need For Speed Most Wanted]] | ||
Revision as of 13:27, 21 June 2011
RTAB-Map : Real-Time Appearance-Based Mapping
Description[edit]
Loop closure detection is the process involved when trying to find a match between the current and a previously visited locations in SLAM (Simultaneous Localization And Mapping). Over time, the amount of time required to process new observations increases with the size of the internal map, which may affect real-time processing. RTAB-Map is a novel real-time loop closure detection approach for large-scale and long-term SLAM. Our approach is based on efficient memory management to keep computation time for each new observation under a fixed time limit, thus respecting real-time limit for long-term operation. Results demonstrate the approach's adaptability and scalability using one custom data set and four standard data sets.
Results[edit]
Note that these results (more recent) may differ from those in the video...
Figure 1: Summary of the loop closures detected on UdeS1Hz data set :
- Green : Loop closures detected
- Yellow : Loop closures rejected
- Red : Unable to detect a loop closure because old places could not be retrieved
Figure 2: Processing time for each image acquired (real-time limit fixed to 700 ms for an image rate of 1 Hz)
Figure 3: Precision-Recall (43% recall at 100% precision)
Source code[edit]
The code was tested on Windows (Xp, 7), Mac OS X 10.6 and Ubuntu 10.4LTS.
- Standalone application, libraries and ROS packages : rtabmap
Data sets[edit]
UdeS1Hz samples[edit]
Rain!
Compare illumination and camera orientation with the next image...
Elevator door...
Downloads[edit]
Community data sets from other loop closure detection approaches :
- Angeli et al. : Lip6Indoor and Lip6Outdoor
- Cummins et al. (FAB-MAP) : NewCollege and CityCentre
Publications
Labbé, M., Michaud, F. (2011), “Memory management approach for real-time appearance-based loop closure detection”, submitted to IEEE Transactions on Robotics.
